Single-cell sequencing-based technologies will revolutionize whole-organism science
Abstract
The unabated progress in next-generation sequencing technologies is fostering a wave of new genomics, epigenomics, transcriptomics and proteomics technologies. These sequencing-based technologies are increasingly being targeted to individual cells, which will allow many new and longstanding questions to be addressed. For example, single-cell genomics will help to uncover cell lineage relationships; single-cell transcriptomics will supplant the coarse notion of marker-based cell types; and single-cell epigenomics and proteomics will allow the functional states of individual cells to be analysed. These technologies will become integrated within a decade or so, enabling high-throughput, multi-dimensional analyses of individual cells that will produce detailed knowledge of the cell lineage trees of higher organisms, including humans. Such studies will have important implications for both basic biological research and medicine.
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Single-cell transcriptomics provides unprecedented profiling depth and scalability, enabling comprehensive quantitative analysis and classification of cell types at scale.
“Single-cell transcriptomics by single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) provides unprecedented profiling depth and scalability, enabling comprehensive quantitative analysis and classification of cell types at scale2,3,7–9.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [9]
t-SNE is a powerful nonlinear dimensionality-reduction alternative, especially when computed with an efficient approximation that scales to large genomic data sets.
“A powerful, nonlinear alternative5,105–107 is t-distributed stochastic neighbor embedding108 (t-SNE; introduced for single-cell analysis as viSNE105), especially when computed with an efficient approximation109 compatible with the scale of large genomic data sets110.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [105]
In leukemic bone marrow, t-SNE embedding of mass cytometry profiles showed some markers in distinct clusters and others in a continuous gradient.
“In leukemic bone marrow105, t-SNE108 embedding of mass cytometry profiles showed that while some markers were expressed in distinct clusters, others formed a continuous gradient.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [105]
The transcriptome of mammalian cells consists of 10^5-10^6 individual mRNA molecules.
“The transcriptome of mammalian cells consists of 105–106 individual messenger RNA (mRNA) molecules (Shapiro et al., 2013).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Shapiro et al., 2013
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- Authors
- Shapiro, E., Biezuner, T., & Linnarsson, S.
- Affiliations
- Weizmann Institute of Science (IL)
- Karolinska Institutet (SE)
- Corresponding author
- Shapiro, Ehud
- Linnarsson, Sten
- Venue
- Nature Reviews Genetics
- Published
- 2013-07-30
- Volume
- 14
- Issue
- 9
- Pages
- 618-630
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- OpenAlex
- W2107680358
- Cited by
- 1,177
- References
- 143
- Citations by year
- 2026: 34 · 2025: 72 · 2024: 85 · 2023: 83 · 2022: 88 · 2021: 95 · 2020: 97 · 2019: 113 · 2018: 99 · 2017: 101 · 2016: 110 · 2015: 108 · 2014: 83 · 2013: 8
- FWCI
- 9.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- European Commission
- Vetenskapsrådet
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell genomics, single-cell transcriptomics, single-cell epigenomics, single-cell proteomics, cell lineage, cell types, multi-omics integration, high-throughput single-cell analysis, cellular heterogeneity
- MeSH terms
- Animals, Cell Separation, Humans, Gene Expression Profiling, Genomics, Proteomics, Epigenomics, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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